41,064 research outputs found
Face Alignment in the Wild.
PhDFace alignment on a face image is a crucial step in many computer vision applications such
as face recognition, verification and facial expression recognition. In this thesis we present
a collection of methods for face alignment in real-world scenarios where the acquisition
of the face images cannot be controlled. We first investigate local based random regression
forest methods that work in a voting fashion. We focus on building better quality
random trees, first, by using privileged information and second, in contrast to using explicit
shape models, by incorporating spatial shape constraints within the forests. We also
propose a fine-tuning scheme that sieves and/or aggregates regression forest votes before
accumulating them into the Hough space. We then investigate holistic methods and propose
two schemes, namely the cascaded regression forests and the random subspace supervised
descent method (RSSDM). The former uses a regression forest as the primitive regressor
instead of random ferns and an intelligent initialization scheme. The RSSDM improves the
accuracy and generalization capacity of the popular SDM by using several linear regressions
in random subspaces. We also propose a Cascaded Pose Regression framework for
face alignment in different modalities, that is RGB and sketch images, based on a sketch
synthesis scheme. Finally, we introduce the concept of mirrorability which describes how
an object alignment method behaves on mirror images in comparison to how it behaves on
the original ones. We define a measure called mirror error to quantitatively analyse the mirrorability
and show two applications, namely difficult samples selection and cascaded face
alignment feedback that aids a re-initialisation scheme. The methods proposed in this thesis
perform better or comparable to state of the art methods. We also demonstrate the generality
by applying them on similar problems such as car alignment.China Scholarship Counci
Face Alignment Assisted by Head Pose Estimation
In this paper we propose a supervised initialization scheme for cascaded face
alignment based on explicit head pose estimation. We first investigate the
failure cases of most state of the art face alignment approaches and observe
that these failures often share one common global property, i.e. the head pose
variation is usually large. Inspired by this, we propose a deep convolutional
network model for reliable and accurate head pose estimation. Instead of using
a mean face shape, or randomly selected shapes for cascaded face alignment
initialisation, we propose two schemes for generating initialisation: the first
one relies on projecting a mean 3D face shape (represented by 3D facial
landmarks) onto 2D image under the estimated head pose; the second one searches
nearest neighbour shapes from the training set according to head pose distance.
By doing so, the initialisation gets closer to the actual shape, which enhances
the possibility of convergence and in turn improves the face alignment
performance. We demonstrate the proposed method on the benchmark 300W dataset
and show very competitive performance in both head pose estimation and face
alignment.Comment: Accepted by BMVC201
Occlusion Coherence: Detecting and Localizing Occluded Faces
The presence of occluders significantly impacts object recognition accuracy.
However, occlusion is typically treated as an unstructured source of noise and
explicit models for occluders have lagged behind those for object appearance
and shape. In this paper we describe a hierarchical deformable part model for
face detection and landmark localization that explicitly models part occlusion.
The proposed model structure makes it possible to augment positive training
data with large numbers of synthetically occluded instances. This allows us to
easily incorporate the statistics of occlusion patterns in a discriminatively
trained model. We test the model on several benchmarks for landmark
localization and detection including challenging new data sets featuring
significant occlusion. We find that the addition of an explicit occlusion model
yields a detection system that outperforms existing approaches for occluded
instances while maintaining competitive accuracy in detection and landmark
localization for unoccluded instances
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